CompBio research

The general area of our scientific research is computational biology, genomics, and proteomics. The goal is to elucidate processes responsible for protein, small molecule, nucleic acid, and interactome structure, function, interaction, evolution, and design so as to understand (and reproduce by computing simulation) how the information encoded by the genome of an organism specifies behaviour and characteristics in the context of its environment.

Specific areas of ongoing research are listed below. Our research leads us to tackle computational problems in algorithmic studies of astronomically large spaces, bioinformatics/data mining, and massively parallel and distributed computing. The work described in the publications is generally encapsulated into a variety of webservers/applications/services (links included) and downloadable software. A full reverse chronologically ordered list of the publications is available as part of my CV. More significant publications are denoted by * along with other annotations such as an accompanying cover or introductory article signifying the notability of a publication so that people may use this as a guide to help focus their studies.

Application

Structural and functional studies of biologically important proteins, systems, and problems. Use the structure and function prediction tools developed by us to help guide experimentalists in manipulating proteins and extracting information about their function and structure in vivo, both at the single molecule as well as at the genomic/systems levels. Some key areas include work on therapeutic (inhibitor) discovery and nanobiotechnology. This work is usually done in collaboration with experimentalists. I list these papers first since they demonstrate a true application of the work we do. In many cases, these are prospective verification (i.e., a prediction is made before the answer is known and verified).

Therapeutics

  1. Mangione W, Falls Z, Samudrala R. Effective holistic characterization of small molecule effects using heterogeneous biological networks. Frontiers in Pharmacolology 14: 1113007, 2023.
  2. Kumari R, Sharma SD, Kumar A, Ende Z, Mishina M, Wang Y, Falls Z, Samudrala R, Pohl J, Knight PR, Sambhara S. Antiviral approaches against influenza virus. Clinical Microbiology Reviews 36: e0004022, 2023.
  3. Bruggemann L, Falls Z, Mangione W, Schwartz SA, Battaglia S, Aalinkeel R, Mahajan SD, Samudrala R. Multiscale analysis and validation of effective drug combinations targeting driver KRAS mutations in non-small cell lung cancer. International Journal of Molecular Sciences 24: 997, 2023. *
  4. Mangione W, Falls Z, Samudrala R. Optimal COVID-19 therapeutic candidate discovery using the CANDO platform. Frontiers in Pharmacology 13: 970494, 2022. *
  5. Moukheiber L, Mangione W, Moukheiber M, Maleki S, Falls Z, Gao M, Samudrala R. Identifying protein features and pathways responsible for toxicity using machine learning and tox21: Implications for predictive toxicology. Molecules 27: 3021, 2022. *
  6. Mammen MJ, Tu C, Morris MC, Richman S, Mangione W, Falls Z, Qu J, Broderick G, Sethi S, Samudrala R. Proteomic network analysis of bronchoalveolar lavage fluid in ex-smokers to discover implicated protein targets and novel drug treatments for chronic obstructive pulmonary disease. Pharmaceuticals 15: 566, 2022. *
  7. Falls Z, Fine J, Chopra G, Samudrala R. Accurate prediction of inhibitor binding to HIV-1 protease using CANDOCK. Frontiers in Chemistry 9: 775513, 2022.
  8. Schuler J, Falls Z, Mangione W, Hudson M, Bruggemann L, Samudrala R. Evaluating performance of drug repurposing technologies. Drug Discovery Today 27: 49-64, 2022. *
  9. Overhoff B, Falls Z, Mangione W, Samudrala R. A deep-learning proteomic-scale approach for drug design. Pharmaceuticals (Basel) 14: 1277, 2021. *
  10. Dey-Rao R, Smith GR, Timilsina U, Falls Z, Samudrala R, Stavrou S, Melendy T. A fluorescence-based, gain-of-signal, live cell system to evaluate SARS-CoV-2 main protease inhibition. Antiviral Research 195: 105183, 2021.
  11. Palanikumar L, Karpauskaite L, Al-Sayegh M, Chehade I, Alam M, Hassan S, Maity D, Ali L, Kalmouni M, Hunashal Y, Ahmed J, Houhou T, Karapetyan S, Falls Z, Samudrala R, Pasricha R, Esposito G, Afzal AJ, Hamilton AD, Kumar S, Magzoub M. Protein mimetic amyloid inhibitor potently abrogates cancer-associated mutant p53 aggregation and restores tumor suppressor function. Nature Communications 12: 3962, 2021.
  12. Hudson ML, Samudrala R. Multiscale virtual screening optimization for shotgun drug repurposing using the CANDO platform. Molecules 26: 2581-2597, 2021.
  13. Chatrikhi R, Feeney CF, Pulvino MJ, Alachouzos G, MacRae AJ, Falls Z, Rai S, Brennessel WW, Jenkins JL, Walter MJ, Graubert TA, Samudrala R, Jurica MS, Frontier AJ, Kielkopf CL. A synthetic small molecule stalls pre-mRNA splicing by promoting an early-stage U2AF2-RNA complex. Cell Chemical Biology 28: 1145-1157, 2021.
  14. Mangione W, Falls Z, Chopra G, Samudrala R. cando.py: Open source software for predictive bioanalytics of large scale drug-protein-disease data. Journal of Chemical Information and Modeling 60: 4131-4136, 2020. *
  15. Mangione W, Falls Z, Melendy T, Chopra G, Samudrala R. Shotgun drug repurposing biotechnology to tackle epidemics and pandemics. Drug Discovery Today 25: 1126-1128, 2020. *
  16. Fine J, Konc J, Samudrala R, Chopra G. CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials. Journal of Chemical Information and Modeling 60: 1509-1527, 2020. *
  17. Fine J, Lackner R, Samudrala R, Chopra G. Computational chemoproteomics to understand the role of selected psychoactives in treating mental health indications. Scientific Reports 9, 1315, 2019. *
  18. Schuler J, Samudrala R. Fingerprinting CANDO: Increased accuracy with structure and ligand based shotgun drug repurposing. ACS Omega 4: 17393-17403, 2019. *
  19. Schuler J, Mangione W, Samudrala R, Ceusters W. Foundations for a realism-based drug repurposing ontology. Proceedings of the 10th International Conference on Biomedical Ontology, 2019.
  20. Falls Z, Mangione W, Schuler J, Samudrala R. Exploration of interaction scoring criteria in the CANDO platform. BMC Research Notes 12: 318, 2019. *
  21. Mangione W, Samudrala R. Identifying protein features responsible for improved drug repurposing accuracies using the CANDO platform: Implications for drug design. Molecules 24: 167, 2019. *
  22. Schuler J, Hudson M, Schwartz D, Samudrala R. A systematic review of computational drug discovery, development, and repurposing for Ebola Virus Disease treatment. Molecules 22: E1777, 2017.
  23. Chopra C, Kaushik S, Elkin PL, Samudrala R. Combating Ebola with repurposed therapeutics using the CANDO platform. Molecules 21: 1537, 2016. *
  24. Craig JK, Risler JK, Loesch KA, Dong W, Baker D, Barrett LK, Subramanian S, Samudrala R, Van Voorhis WC. Mycobacterium cytidylate kinase appears to be an undruggable target. Journal of Biomolecular Design 21: 695-700, 2016.
  25. Chopra G, Samudrala R. Exploring polypharmacology in drug discovery and repurposing using the CANDO platform. Current Pharmaceutical Design 22: 3109-3123 2016.
  26. Manocheewa S, Mittler JE, Samudrala R, Mullins JI. Composite sequence-structure stability models as screening tools for identifying vulnerable targets for HIV drug and vaccine development. Viruses 7: 5718-5735, 2015.
  27. Sethi G, Chopra G, Samudrala R. Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the CANDO platform. Mini Reviews in Medicinal Chemistry, 15: 705-717, 2015.
  28. Minie M, Chopra G, Sethi G, Horst J, White G, Roy A, Hatti K, Samudrala R. CANDO and the infinite drug discovery frontier. Drug Discovery Today 19: 1353-1363, 2014. *
  29. Lertkiatmongkol P, Assawamakin A, White G, Chopra G, Rongnoparut P, Samudrala R, Tongsima S. Distal effect of amino acid substitutions in CYP2C9 polymorphic variants causes differences in interatomic interactions against (S)-warfarin. PLoS One 8: e74053, 2013.
  30. Strategic protein target analysis for developing drugs to stop dental caries. Horst JA, Pieper U, Sali A, Zhan L, Chopra G, Samudrala R, Featherstone JD. Advances in Dental Research 24: 86-93, 2012. *
  31. Horst JA, Laurenzi A, Bernard B, Samudrala R. Computational multitarget drug discovery. Polypharmacology 263-301, 2012. *
  32. Nicholson CO, Costin JM, Rowe DK, Lin L, Jenwitheesuk E, Samudrala R, Isern S, Michael SF. Viral entry inhibitors block dengue antibody-dependent enhancement in vitro. Antiviral Research 89: 71-74 2010. *
  33. Movahedzadeh F, Balaubramanian V, Bernard B, Iyer S, Samudrala R, Franzblau SG, Balganesh TS. Anti-tuberculosis agents: A rational approach for discovery and development. Genomic and computational tools for emerging infectious diseases, 2010.
  34. Costin JM, Jenwitheesuk E, Lok S-M, Hunsperger E, Conrads KA, Fontaine KA, Rees CR, Rossmann MG, Isern S, Samudrala R, Michael SF. Structural optimization and de novo design of dengue virus entry inhibitory peptides. PLoS Neglected Tropical Diseases 4: e721, 2010. *
  35. Bernard B, Samudrala R. A generalized knowledge-based discriminatory function for biomolecular interactions. Proteins: Structure, Function, and Bioinformatics 76: 115-128, 2009.
  36. Jenwitheesuk E, Horst JA, Rivas K, Van Voorhis WC, Samudrala R. Novel paradigms for drug discovery: Computational multitarget screening. Trends in Pharmacological Sciences 29: 62-71, 2008. [Accompanying cover.] *
  37. Samudrala R, Jenwitheesuk E. Identification of potential HIV-1 targets of minocycline. Bioinformatics 23: 2797-2799, 2007.
  38. Wang K, Mittler J, Samudrala R. Comment on "Evidence for positive epistatis in HIV-1". Science 312: 848b, 2006.
  39. Jenwitheesuk E, Samudrala R. Identification of potential multitarget antimalarial drugs. Journal of the American Medical Association 294: 1490-1491, 2005. *
  40. Jenwitheesuk E, Samudrala R. Heptad-repeat-2 mutations enhance the stability of the enfuvirtide-resistant HIV-1 gp41 hairpin structure. Antiviral Therapy 10: 893-900, 2005. *
  41. Jenwitheesuk E, Wang K, Mittler J, Samudrala R. PIRSpred: A webserver for reliable HIV-1 protein-inhibitor resistance/susceptibility prediction. Trends in Microbiology 13: 150-151, 2005.
  42. Jenwitheesuk E, Samudrala R. Virtual screening of HIV-1 protease inhibitors against human cytomegalovirus protease using docking and molecular dynamics. AIDS 19: 529-533, 2005.
  43. Jenwitheesuk E, Samudrala R. Prediction of HIV-1 protease inhibitor resistance using a protein-inhibitor flexible docking approach. Antiviral Therapy 10: 157-166, 2005.
  44. Jenwitheesuk E, Wang K, Mittler J, Samudrala R. Improved accuracy of HIV-1 genotypic susceptibility interpretation using a consensus approach. AIDS 18: 1858-1859, 2004.
  45. Jenwitheesuk E, Samudrala R. Identifying inhibitors of the SARS coronavirus proteinase. Bioorganic & Medicinal Chemistry Letters 13: 3989-3992, 2003. [Most Cited Paper 2003 - 2006 Award.] *
  46. Jenwitheesuk E, Samudrala R. Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations. BMC Structural Biology 3: 2, 2003. *
  47. Wang K, Jenwitheesuk E, Samudrala R, Mittler J. Simple linear model provides highly accurate genotypic predictions of HIV-1 drug resistance. Antiviral Therapy 9: 343-352, 2004.
  48. Wang K, Samudrala R, Mittler J. Weak agreement between predictions of ``reduced susceptibility'' from Antivirogram and PhenoSense assays. Journal of Clinical Microbiology 42: 2353-2354, 2004.
  49. Wang K, Samudrala R, Mittler J. HIV-1 genotypic drug resistance interpretation algorithms need to include hypersusceptibility mutations. Journal of Infectious Diseases 190: 2055-2056, 2004.
  50. Wang K, Samudrala R, Mittler J. Antivirogram or PhenoSense: a comparison of their reproducibility and an analysis of their correlation. Antiviral Therapy 9: 703-712, 2004.
  51. Protein inhibitor resistance/susceptibility prediction (PIRSpred) web server module
  52. Computational analysis of novel drug opportunities (CANDO) platform

Nanobiotechnology

  1. Cementomimetics-constructing a cementum-like biomineralized microlayer via amelogenin-derived peptides. Gungormus M, Oren EE, Horst JA, Fong H, Hnilova M, Somerman MJ, Snead ML, Samudrala R, Tamerler C, Sarikaya M. International Journal of Oral Sciences 2: 69-77, 2012. *
  2. Notman R, Oren EE, Tamerler C, Sarikaya M, Samudrala R, Walsh TR. Solution study of engineered quartz binding peptides using replica exchange molecular dynamics. Biomacromolecules 11: 3266-3274, 2010.
  3. Oren EE, Notman R, Kim IW, Evans J, Walsh T, Samudrala R, Tamerer C, Sarikaya M. Probing the molecular mechanisms of quartz-binding peptides. Langmuir 26: 11003-11009, 2010.
  4. Samudrala R, Oren EE, Cheng C, Horst, J, Bernard B, Gungormus M, Hnilova M, Fong H, Tamerler C, Sarikaya M. Knowledge-based design of inorganic binding peptides. Proceedings of the conference on the Foundations of Nanoscience: Self-Assembled Architectures and Devices, 2008.
  5. Evans JS, Samudrala R, Walsh TR, Oren EE, Tamerler C. Molecular design of inorganic-binding polypeptides. MRS Bulletin 33: 514-518, 2008. [Accompanying cover and introductory article with biographies on pages 504-512.] *
  6. Oren EE, Tamerler C, Sahin D, Hnilova M, Seker UOS, Sarikaya M, Samudrala R. A novel knowledge-based approach for designing inorganic binding peptides. Bioinformatics 23: 2816-2822, 2007. *

General and specific functional studies

  1. Bruggemann L, Hawthorne C, Samudrala R, Lopez-Campos GH. Linking genome and exposome: Computational analysis of human variation in chemical-target interactions. Student Health Technology Informatics 270: 1331-1332, 2020.
  2. Mandloi S, Falls Z, Deng R, Samudrala R, Elkin PL. Association of C>U RNA editing with human disease variants. Student Health Technology Informatics 270: 1205-1206, 2020.
  3. Homo-dimerization and ligand binding by the leucine-rich repeat domain at RHG1/RFS2 underlying resistance to two soybean pathogens. Afzal AJ, Srour A, Goil A, Vasudaven S, Liu T, Samudrala R, Dogra N, Kohli P, Malakar A, Lightfoot DA. BMC Plant Biology 13: 43, 2013.
  4. Self-assembly of filamentous amelogenin requires calcium and phosphate: from dimers via nanoribbons to fibrils. Martinez-Avila O, Wu S, Kim SJ, Cheng Y, Khan F, Samudrala R, Sali A, Horst JA, Habelitz S. Biomacromolecules 13: 3494-502, 2012.
  5. An P, Li R, Wang JM, Yoshimura T, Takahashi M, Samudrala R, O'Brien SJ, Phair J, Goedert JJ, Kirk GD, Troyer JL, Sezgin E, Buchbinder SP, Donfield S, Nelson GW, Winkler CA. Role of exonic variation in chemokine receptor genes on AIDS: CCRL2 F167Y association with pneumocystis pneumonia. PLoS Genetics 7: e1002328, 2011.
  6. Horst OV, Horst JA, Samudrala R, Dale BA. Caries induced cytokine network in the odontoblast layer of human teeth. BMC Immunology 12: 9, 2011.
  7. Cunningham ML, Horst JA, Rieder MJ, Hing AV, Stanaway IB, Park SS, Samudrala R, Speltz ML. IGF1R variants associated with isolated single suture craniosynostosis. The American Journal of Human Genetics 155A: 91-97, 2011. [Accompanying cover.]
  8. Borlee BR, Goldman AD, Murakami K, Samudrala R, Wozniak DJ, Parsek MR. Pseudomonas aeruginosa uses a cyclic-di-GMP-regulated adhesin to reinforce the biofilm extracellular matrix. Molecular Microbiology 75: 827-842, 2010. [Accompanying cover.]
  9. Goldman AD, Leigh JA, Samudrala R. Comprehensive computational analysis of Hmd enzymes and paralogs in methanogenic Archaea. BMC Evolutionary Biology 9: 199, 2009.
  10. Jenkins C, Samudrala R, Geary S, Djordjevic SP. Structural and functional characterisation of an organic hydroperoxide resistance (Ohr) protein from Mycoplasma gallisepticum. Journal of Bacteriology 190: 2206-2208, 2008.
  11. Chevance FFV, Takahashi N, Karlinsey JE, Gnerer J, Hirano T, Samudrala R, Aizawa S-I, Hughes KT. The mechanism of outer membrane penetration by the eubacterial flagellum and implications for spirochete evolution. Genes and Development 21: 2326-2335, 2007.
  12. Bockhorst J, Lu F, Janes JH, Keebler J, Gamain B, Awadalla P, Su X, Samudrala R, Jojic N, Smith JD. Structural polymorphism and diversifying selection on the pregnancy malaria vaccine candidate VAR2CSA. Molecular and Biochemical Parasitology 155: 103-112, 2007.
  13. Berube PM, Samudrala R, Stahl DA. Transcription of amoC is associated with the recovery of Nitrosomonas europaea from ammonia starvation. Journal of Bacteriology 89: 3935-3944, 2007.
  14. Korotkova N, Le Trong I, Samudrala R, Korotkov K, Van Loy CP, Bui A-L, Moseley SL, Stenkamp RE. Crystal structure and mutational analysis of the DaaE adhesin of Escherichia coli. Journal of Biological Chemistry 281: 22367-22377, 2006.
  15. Howell DPG, Samudrala R, Smith JD. Disguising itself - insights into Plasmodium falciparum binding and immune evasion from the DBL crystal structure. Molecular and Biochemical Parasitology 148: 1-9, 2006.
  16. Wang W, Zheng H, Yang S, Yu H, Li J, Jiang H, Su J, Yang L, Zhang J, McDermott J, Samudrala R, Wang J, Yang H, Yu J, Kristiansen K, Wong GK, Wang J. Origin and evolution of new exons in rodents. Genome Research 15: 1258-1264, 2005.
  17. Liu T, Jenwitheesuk E, Teller D, Samudrala R. Structural insights into the Cellular Retinaldehyde Binding Protein (CRALBP). Proteins: Structure, Function, and Bioinformatics 61: 412-422, 2005.
  18. Ekwa-Ekok C, Diaza GA, Carlson C, Hasegawad T, Samudrala R, Limf K, Yabug JM, Levya B, Schnapp LM. Genomic organization and sequence variation of the human integrin subunit 8 gene (ITGA8). Matrix Biology 23: 487-496, 2004.
  19. Wang J, Zhang J, Zheng H, Li J, Liu D, Li H, Samudrala R, Yu J, Wong GK. Mouse transcriptome: Neutral evolution of "non-coding" complementary DNAs. Nature 431, 2004.
  20. Jenkins C, Samudrala R, Anderson I, Hedlund BP, Petroni G, Michailova N, Pinel N, Overbeek R, Rosati G, Staley JT. Genes for the cytoskeletal protein tubulin in the bacteria genus Prosthecobacter. Proceedings of the National Academy of Sciences 99: 17049-17054, 2002.
  21. Van Loy CP, Sokurenko EP, Samudrala R, Moseley S. Identification of a DAF binding domain in the Dr adhesin. Molecular Microbiology 45: 439-452, 2002.
  22. Samudrala R, Xia Y, Levitt M, Cotton NJ, Huang ES, Davis R. Probing structure-function relationships of the DNA polymerase alpha-associated zinc-finger protein using computational approaches. In Altman R, Dunker K, Hunter L, Klein T, Lauderdale K, eds. Proceedings of the Pacific Symposium on Biocomputing 179-189, 2000.
  23. Protinfo structure, function, and interaction prediction server

Evolution

We use our prediction protocols to explore early evolution and origin of life issues.

  1. Goldman AD, Barrows J, Samudrala R. The enzymatic and metabolic capabilities of early life. PLoS One 7: e39912, 2012. *
  2. Goldman AD, Horst JA, Hung L-H, Samudrala R. Evolution of the protein repertoire. Systems Biology: 207-237, 2012. (R Meyers, Editor. Wiley-VCH Wienheim, Germany.)
  3. Goldman AD, Samudrala R, Barrows J. The evolution and functional repertoire of translation proteins following the origin of life. Biology Direct 5: 15, 2010. *
  4. Goldman AD, Leigh JA, Samudrala R. Comprehensive computational analysis of Hmd enzymes and paralogs in methanogenic Archaea. BMC Evolutionary Biology 9: 199, 2009.

Systems

Application and integration of single molecule structure and function prediction techniques to whole genomes and proteomes in an integrated manner. Combine single molecule and genomic/proteomic data to to explore the relationships among the molecular and organismal (systems) worlds and create a comprehensive picture of the relationship between genotype and phenotype.

  1. Hung L-H, Samudrala R. Rice protein models from the Nutritious Rice for the World Project. bioRxiv 091975; doi: https://doi.org/10.1101/091975, 2016.
  2. Minie M. Samudrala R. The promise and challenge of digital biology. Journal of Bioengineering and Biomedical Sciences 3: e118, 2013. editorial.
  3. Matasci N, Hung L-H, ..., Samudrala R, Tian Z, Wu X, Sun X, Zhang Y, Wang J, Leebens-Mack J, Wong GSK. Data access for the 1,000 Plants (1KP) project. Gigascience 3: 17, 2014.
  4. McDermott J, Ireton R, Montgomery K, Bumgarner R, Samudrala R (editors). Computational systems biology. Methods in Molecular Biology 541: v-ix, 2009. *
  5. Frazier Z, McDermott J, Samudrala R. Computational representation of biological systems. Methods in Molecular Biology 541: 535-549, 2009.
  6. Guerquin M, McDermott J, Samudrala R. The Bioverse API and Web Application. Methods in Molecular Biology 541: 511-534, 2009.
  7. Rashid I, McDermott J, Samudrala R. Inferring molecular interaction pathways from eQTL data. Methods in Molecular Biology 541: 211-223, 2009.
  8. Wichadakul D, McDermott J, Samudrala R. Prediction and integration of regulatory and protein-protein interactions. Methods in Molecular Biology 541: 101-143, 2009.
  9. McDermott J, Wang J, Yu J, Wong GSK, Samudrala R. In Rao GP, Wagner C, Singh RK, editors. Prediction and annotation of plant protein interaction networks. Application of Genomics and Bioinformatics in Plants (Studium Press) 207-238, 2008.
  10. McDermott J, Samudrala R. Bioinformatic characterization of plant networks. Proceedings of the Asia Pacific Conference on Plant Tissue Culture and Agrobiotechnology, 2007.
  11. Chang AN, McDermott J, Guerquin M, Frazier Z, Samudrala R. Integrator: Interactive graphical search of large protein interactomes over the Web. BMC Bioinformatics 7: 146, 2006.
  12. McDermott J, Bumgarner RE, Samudrala R. Functional annotation from predicted protein interaction networks. Bioinformatics 21: 3217-3226, 2005. *
  13. McDermott J, Guerquin M, Frazier Z, Chang AN, Samudrala R. BIOVERSE: Enhancements to the framework for structural, functional, and contextual annotations of proteins and proteomes. Nucleic Acids Research 33: W324-W325, 2005. *
  14. Chang AN, McDermott J, Samudrala R. An enhanced java graph applet interface for visualizing interactomes. Bioinformatics 21: 1741-1742, 2005.
  15. Yu J, Wang J, Lin W, Li S, Li H, Zhou J, ..., McDermott J, Samudrala R, Wang J, Wong GK. The genomes of Oryza sativa: A history of duplications. PLoS Biology 3: e38, 2005. *
  16. McDermott J, Samudrala R. Enhanced functional information from protein networks. Trends in Biotechnology 22: 60-62, 2004. *
  17. McDermott J, Samudrala R. BIOVERSE: Functional, structural, and contextual annotation of proteins and proteomes. Nucleic Acids Research 31: 3736-3737, 2003. *
  18. McDermott J, Samudrala R. The Bioverse: An object-oriented genomic database and webserver written in Python. In Proceedings of the conference on Objects in Bio- & Chem-Informatics, 2002.
  19. Bioverse framework
  20. Protinfo structure, function, and interaction prediction server

Interaction

Methods for predicting interactions between molecules.

  1. Kittichotirat W, Guerquin M, Bumgarner RE, Samudrala R. Protinfo PPC: A web server for atomic level prediction of protein complexes. Nucleic Acids Research 37: W519-W525, 2009. *
  2. Bernard B, Samudrala R. A generalized knowledge-based discriminatory function for biomolecular interactions. Proteins: Structure, Function, and Bioinformatics 76: 115-128, 2009. *
  3. McDermott J, Bumgarner RE, Samudrala R. Functional annotation from predicted protein interaction networks. Bioinformatics 21: 3217-3226, 2005. *
  4. McDermott J, Samudrala R. Enhanced functional information from protein networks. Trends in Biotechnology 22: 60-62, 2004.
  5. Bioverse framework

Function

Generally applicable methods for predicting protein function from sequence and/or structure.

  1. McDermott JE, Corrigan A, Peterson E, Oehmen C, Niemann G, Cambronne ED, Sharp D, Adkins JN, Samudrala R, Heffron F. Computational prediction of type III and IV secreted effectors in Gram-negative bacteria. Infection and Immunity 79: 23-32, 2010.
  2. Horst JA, Wang K, Horst OV, Cunningham ML, Samudrala R. Disease risk of missense mutations using structural inference from predicted function. Current Protein & Peptide Science 11: 573-588, 2010.
  3. Horst J, Samudrala R. A protein sequence meta-functional signature for calcium binding residue prediction. Pattern Recognition Letters 31: 2103-2112, 2010. *
  4. Samudrala R, Heffron F, McDermott J. In silico identification of secreted effectors in Salmonella typhimurium. PLoS Pathogens 5: e1000375, 2009. *
  5. Wang K, Horst J, Cheng G, Nickle D, Samudrala R. Protein meta-functional signatures from combining sequence, structure, evolution and amino acid property information. PLoS Computational Biology 4: e1000181, 2008. *
  6. Wang K, Samudrala R. Incorporating background frequency improves entropy-based residue conservation measures. BMC Bioinformatics 7: 385, 2006.
  7. Wang K, Samudrala R. Automated functional classification of experimental and predicted protein structures. BMC Bioinformatics 7: 278, 2006. *
  8. Cheng G, Qian B, Samudrala R, Baker D. Improvement in protein functional site prediction by distinguishing structural and functional constraints on protein family evolution using computational design. Nucleic Acids Research 33: 5861-5867, 2005. *
  9. McDermott J, Bumgarner RE, Samudrala R. Functional annotation from predicted protein interaction networks. Bioinformatics 21: 3217-3226, 2005. *
  10. Wang K, Samudrala R. FSSA: A novel method for identifying functional signatures from structural alignments. Bioinformatics 21: 2969-2977, 2005. *
  11. McDermott J, Samudrala R. Enhanced functional information from protein networks. Trends in Biotechnology 22: 60-62, 2004.
  12. Protinfo structure, function, and interaction prediction server

Structure

De novo protein structure prediction

The basic paradigm is to sample the conformational space exhaustively or semi-exhaustively such that native-like conformations are observed. These conformations are selected using the all-atom based scoring functions. Some methods have had good success in the CASP blind prediction experiments.

  1. Laurenzi A, Hung L-H, Samudrala R. Structure prediction of partial length protein sequences: applications in foldability prediction and EST annotation. International Journal of Molecular Sciences 214: 14892-14907, 2013.
  2. Liu T, Horst J, Samudrala R. A novel method for predicting and using distance constraints of high accuracy for refining protein structure prediction. Proteins: Structure, Function, and Bioinformatics 77: 220-234, 2009. *
  3. Horst J, Samudrala R. Diversity of protein structures and difficulties in fold recognition: The curious case of Protein G. F1000 Biology Reports 1:69, 2009. *
  4. Hung L-H, Ngan S-C, Samudrala R. De novo protein structure prediction. In Xu Y, Xu D, Liang J, editors. Computational Methods for Protein Structure Prediction and Modeling 2: 43-64, 2007.
  5. Hung L-H, Ngan S-C, Liu T, Samudrala R. PROTINFO: New algorithms for enhanced protein structure prediction. Nucleic Acids Research 33: W77-W80, 2005. *
  6. Hung L-H, Samudrala R. PROTINFO: Secondary and tertiary protein structure prediction. Nucleic Acids Research 31: 3296-3299, 2003. *
  7. Samudrala R, Levitt M. A comprehensive analysis of 40 blind protein structure predictions. BMC Structural Biology 2: 3-18, 2002. *
  8. Samudrala R. Lessons from blind protein structure prediction experiments. In Grohima M, Selvaraj S, eds. Recent Research Developments in Protein Folding, Stability, and Design, 123-139, 2002.
  9. Xia Y, Huang ES, Levitt M, Samudrala R. Ab initio construction of protein tertiary structures using a hierarchical approach. Journal of Molecular Biology, 300: 171-185, 2000. *
  10. Samudrala R, Xia Y, Levitt M. Huang ES. Ab initio prediction of protein structure using a combined hierarchical approach. Proteins: Structure, Function, and Genetics S3: 194-198, 1999. *
  11. Huang ES, Samudrala R, Ponder JW. Ab initio protein structure prediction results using a simple distance geometry method. unpublished.
  12. Huang ES, Samudrala R, Ponder JW. Ab initio fold prediction of small helical proteins using distance geometry and knowledge-based scoring functions. Journal of Molecular Biology 290:267-281, 1999.
  13. Huang ES, Samudrala R, Ponder JW. Distance geometry generates native-like folds for small helical proteins using the consensus distances of predicted protein structures. Protein Science 7: 1998-2003, 1998.
  14. Samudrala R, Xia Y, Levitt M, Huang ES. A combined approach for ab initio construction of low resolution protein tertiary structures from sequence. In Altman R, Dunker K, Hunter L, Klein T, Lauderdale K, eds. Proceedings of the Pacific Symposium on Biocomputing 505-516, 1999.
  15. Protinfo structure, function, and interaction prediction server

Comparative modelling of protein structure

Handling the problem of context sensitivity in protein structures. Some methods have had good success in the CASP blind prediction experiments.

  1. Bondoc JMG, Gutka HJ, Almutairi MM, Patwell R, Rutter MW, Wolf NM, Samudrala R, Mehboob S, Dementiev A, Abad-Zapatero C, Movahedzadeh F. Rv0100, a proposed acyl carrier protein in Mycobacterium tuberculosis: expression, purification and crystallization. Corrigendum. Acta Crystallograpica F Structural Biology Communications 76: 192-193, 2020.
  2. Bondoc JMG, Gutka HJ, Almutairi MM, Patwell R, Rutter MW, Wolf NM, Samudrala R, Mehboob S, Movahedzadeh F. Rv0100, a proposed acyl carrier protein in Mycobacterium tuberculosis: expression, purification and crystallization. Acta Crystallograpica F Structural Biology Communications. 75: 646-651, 2019.
  3. Kittichotirat W, Guerquin M, Bumgarner RE, Samudrala R. Protinfo PPC: A web server for atomic level prediction of protein complexes. Nucleic Acids Research 37: W519-W525, 2009.
  4. Liu T, Horst J, Samudrala R. A novel method for predicting and using distance constraints of high accuracy for refining protein structure prediction. Proteins: Structure, Function, and Bioinformatics 77: 220-234, 2009.
  5. Liu T, Guerquin M, Samudrala R. Improving the accuracy of template-based predictions by mixing and matching between initial models. BMC Structural Biology 8: 24, 2008.
  6. Hung L-H, Ngan S-C, Liu T, Samudrala R. PROTINFO: New algorithms for enhanced protein structure prediction. Nucleic Acids Research 33: W77-W80, 2005.
  7. Hung L-H, Samudrala R. PROTINFO: Secondary and tertiary protein structure prediction. Nucleic Acids Research 31: 3296-3299, 2003. *
  8. Samudrala R, Levitt M. A comprehensive analysis of 40 blind protein structure predictions. BMC Structural Biology 2: 3-18, 2002. *
  9. Samudrala R. Lessons from blind protein structure prediction experiments. In Grohima M, Selvaraj S, eds. Recent Research Developments in Protein Folding, Stability, and Design, 123-139, 2002.
  10. Samudrala R, Moult J. A graph-theoretic algorithm for comparative modelling of protein structure. Journal of Molecular Biology 279: 287-302, 1998. *
  11. Samudrala R, Moult J. Handling context-sensitivity in protein structures using graph theory: bona fide prediction Proteins: Structure, Function, and Genetics 29S: 43-49, 1997. *
  12. Samudrala R. A graph-theoretic solution to the context-sensitivity problem in protein structure prediction. Ph.D. thesis, 1997.
  13. Samudrala R, Pedersen JT, Zhou H, Luo R, Fidelis K, Moult J. Confronting the problem of interconnected structural changes in the comparative modelling of proteins. Proteins: Structure, Function, and Genetics 23: 327-336, 1995.
  14. Protinfo structure, function, and interaction prediction server

Protein structure from combining theory and experiment

Use the structure prediction methods described below with experimental data to produce better results.

  1. Hung L-H, Samudrala R. An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data. Journal of Biomolecular NMR 36: 189-198, 2006. *
  2. Hung L-H, Samudrala R. PROTINFO: Secondary and tertiary protein structure prediction. Nucleic Acids Research 31: 3296-3299, 2003. *
  3. Hung L-H, Samudrala R. Accurate and automated classification of protein secondary structure with PsiCSI. Protein Science 12: 288-295, 2003. *
  4. Protinfo structure, function, and interaction prediction server

Scoring/discriminatory functions for protein structure prediction

We primarily use an all-atom distance dependent conditional probability discriminatory function that is surprisingly accurate at selecting correct from incorrect protein conformations. It is used both for ab initio prediction and comparative modelling. We also use a number of other scoring functions as filters, and also develop databases of incorrect conformations ("decoys") to help evaluate scoring functions.

  1. Moughon S, Samudrala R. LoCo: a new backbone-only scoring function for protein structure prediction. BMC Bioinformatics 12: 368, 2011.
  2. Bernard B, Samudrala R. A generalized knowledge-based discriminatory function for biomolecular interactions. Proteins: Structure, Function, and Bioinformatics 76: 115-128, 2009.
  3. Ngan S-C, Hung L-H, Liu T, Samudrala R. Scoring functions for de novo protein structure prediction revisited. Methods in Molecular Biology 413: 243-282, 2007.
  4. Liu T, Samudrala R. The effect of experimental resolution on the performance of knowledge-based discriminatory functions for protein structure selection. Protein Engineering, Design and Selection 19: 431-437, 2006.
  5. Ngan S-C, Inouye M, Samudrala R. A knowledge-based scoring function based on residue triplets for protein structure prediction. Protein Engineering, Design and Selection 19: 187-193, 2006.
  6. Wang K, Fain B, Levitt M, Samudrala R. Improved protein structure selection using decoy-dependent discriminatory functions. BMC Structural Biology 4: 8, 2004. *
  7. Samudrala R, Levitt M. Decoys 'R' Us: A database of incorrect protein conformations for evaluating scoring functions. Protein Science, 9: 1399-1401, 2000.
  8. Huang ES, Samudrala R, Park BH. Scoring functions for ab initio folding. In Walker J, Webster D, eds. Predicting Protein Structure: Methods and Protocols Humana Press, 2000.
  9. Samudrala R, Moult J. An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. Journal of Molecular Biology 275: 893-914, 1998. *
  10. Decoys 'R' Us database

Side chain prediction

There are two papers in this area. The first is a work on exactly what it is that primarily determines side chain conformational preferences in proteins. The main thrust here is the use of the discriminatory function to select the most probable side chain rotamers given a large number of possible conformations. The second paper compares different methods for side chain prediction.

  1. Samudrala R, Huang ES, Koehl P, Levitt M. Side chain construction on non-native main chains using an all-atom discriminatory function. Protein Engineering, 7: 453-457, 2000.
  2. Samudrala R, Moult J. Determinants of side chain conformational preferences in protein structures. Protein Engineering 11: 991-997, 1998.

Infrastructure

We prefer to make our clusters from cheap components that can be readily discarded, and prefer to completely decentralise our systems. Also included in this category are algorithms developed to handle the scientific problems we face.

  1. Hung L-H, Samudrala R. fast_protein_cluster: parallel and optimized clustering of large scale protein modeling data. Bioinformatics 30: 1774-1776, 2014.
  2. Hung L-H, Samudrala R. Accelerated protein structure comparison using TM-score-GPU. Bioinformatics 28: 2191-2192, 2012.
  3. Hung LH, Guerquin M, Samudrala R. GPU-Q-J, a fast method for calculating root mean square deviation (RMSD) after optimal superposition. BMC Research Notes 4: 97, 2011.
  4. Frazier Z, McDermott J, Samudrala R. Computational representation of biological systems. Methods in Molecular Biology 541: 535-549, 2009.
  5. Guerquin M, McDermott J, Samudrala R. The Bioverse API and Web Application. Methods in Molecular Biology 541: 511-534, 2009.
  6. Samudrala R. Taking the cost out of firewalls. LinuxWorld Magazine 1: 58-59, 2003.
  7. Samudrala R. Linux Cluster HOWTO, 2003.
  8. McDermott J, Samudrala R. The Bioverse: An object-oriented genomic database and webserver written in Python. In Proceedings of the conference on Objects in Bio- & Chem-Informatics, 2002.
  9. Samudrala R. Installing and using RAID. In Danesh A, Gautam D, eds. Special Edition Using Linux System Administration, Que Publishing, 2000.
Samudrala Computational Biology Research Group (CompBio) || Ram Samudrala || me@ram.org